| Title: | A Mendelian Randomization Analysis of Healthy Habits and Outcomes Using International Genomic Cohorts. |
| Lead Institution: | Kyoto University |
| Principal investigator: |
Application 148863
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About
Aims: We aim to investigate and clarify the direct links between health habits and their effects on cardiovascular and renal risks. Using the Mendelian randomization method, we hope to gain a clearer understanding of these connections, which traditional studies might miss due to possible biases. Scientific Rationale: It's often challenging in contemporary health research to clearly determine the direct links between habits and health outcomes. Our approach is different because we use Mendelian randomization, a method that uses genetic differences as tools, to identify direct causes without the usual interfering factors. We plan to use the detailed data from the UK Biobank, especially the GWAS data, to find specific genetic markers linked to health habits. With this genetic information and a wide range of data on diet, health, and background factors, we can create studies that mimic randomized controlled trials. This will help us estimate the direct effects of health habits on heart and kidney health outcomes. The broad and detailed nature of the UK Biobank data means our analysis can consider a wide range of diets, backgrounds, and health outcomes. Project Duration: We expect the project to last three years. The first year will be about gathering data and an initial analysis. The next year will focus on the Mendelian randomization and more detailed studies. The last year will be for interpreting the data, confirming our results, and sharing our findings. Public Health Impact: The results of our study could be very important for public health. With growing concerns about heart and kidney diseases, it's essential to understand the direct links between our daily habits and these health problems. Using the detailed UK Biobank data, we aim to give more specific and evidence-based health advice. By creating health improvement programs and personalized recommendations based on our findings, we hope to improve public health and well-being. This study could also guide future health research by showing the importance of using wide-ranging and detailed data for creating evidence-based health advice. In Conclusion: For our study to be successful, we need to access various types of information from the UK Biobank. This request highlights the importance of having a wide range of data. It shows that a thorough approach, based on detailed and varied data, can produce strong health research that can be applied in many ways.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 18582 | Nonparametric Bayesian Adjustment of Unmeasured Confounders in Cox Proportional Hazards Models | Shunichiro Orihara (+5) | 2026 | Statistics in Medicine |
Enabling scientific discoveries that improve human health